Prediction of personality traits based on electroencephalogram pattern in healthy people

According to the theory of sensitivity to reinforcement, behavioral differences of people in sensitivity to reward and avoidance of punishment are the basis of stable personality, mood, and mental and cognitive differences. People with a behavioral inhibition system turn to defense, inhibition, and avoidance when faced with a threatening situation. Another group of behavioral activation systems is looking for positive motivations. Reward-sensitive people tend to drive, make risky decisions, and engage in risky health behaviors. Based on this, there is a research gap in the field of using neurophysiology methods in determining behavioral activation and inhibition systems and possibly the relationship between alpha power and activation of behavioral brain systems and a model that can predict the type of behavioral brain system of people.

Considering the research gap, this study is designed and implemented with the aim of providing a neurophysiology method to determine the behavioral brain systems based on the data obtained from the electroencephalography analysis of people.

The research method is correlational and based on regression equation modeling. A sample of 300 students of the Faculty of Educational Sciences and Psychology of Tabriz University were randomly screened using Carver and Wright's questionnaire (1994) and 15 subjects were assigned to the activation group and another 15 subjects were assigned to the behavioral inhibition group. The selection criterion was to obtain a score of more than one standard deviation higher than the average in inhibition and activation scores. Brain waves were recorded with a 19-channel Norscan amplifier based on the international 10-20 system.

The statistical analysis of the results showed that the average alpha power in two groups with different behavioral core systems is statistically significant (p<0.05). In addition, the correlation between alpha power and behavioral brain systems was statistically significant at the p<0.05 level. Then, a model was determined using regression analysis, and the coefficients were significant at the p<0.01 level.

The results of the present study indicate that a linear regression model has been obtained. The model was able to significantly separate the behavioral inhibition group from the behavioral activation group. After confirmation with similar studies, the model can be used as a neurophysiological tool in business centers for employment, as well as in counseling, psychotherapy, and psychiatric clinics.